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Structure-constrained molecular generation is a promising approach to drug discovery. The goal of structure-constrained molecular generation is to produce a novel molecule that is similar to a given source molecule (e.g. hit molecules) but has enhanced chemical properties (for lead optimization). Many structure-constrained molecular generation models with superior performance in improving chemical properties have been proposed; however, they still have difficulty producing many novel molecules that satisfy both the high structural similarities to each source molecule and improved molecular properties.

We propose a structure-constrained molecular generation model that utilizes contractive and margin loss terms to simultaneously achieve property improvement and high structural similarity. The proposed model has two training phases; a generator first learns molecular representation …